TY - JOUR
T1 - Penalized regression with multiple sources of prior effects
AU - Rauschenberger, Armin
AU - Landoulsi, Zied
AU - van de Wiel, Mark A.
AU - Glaab, Enrico
N1 - Funding Information: This work was supported by the Luxembourg National Research Fund (FNR) for the project ‘Clinnova—a trans-regional digital health effort’ [NCER/23/16695277]; and the project DIGIPD [INTER/ERAPerMed 20/14599012] as part of the European Union’s Horizon 2020 Research and Innovation Program. The National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD) is funded by the FNR [FNR/NCER13/BM/11264123]. Publisher Copyright: © 2023 The Author(s). Published by Oxford University Press.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Motivation: In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies. Results: We propose an approach for integrating multiple sources of such prior information into penalized regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application.
AB - Motivation: In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies. Results: We propose an approach for integrating multiple sources of such prior information into penalized regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application.
UR - http://www.scopus.com/inward/record.url?scp=85179126576&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/bioinformatics/btad680
DO - https://doi.org/10.1093/bioinformatics/btad680
M3 - Article
C2 - 37951587
SN - 1367-4803
VL - 39
JO - Bioinformatics (Oxford, England)
JF - Bioinformatics (Oxford, England)
IS - 12
M1 - btad680
ER -